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About This Role
Description
About Norstella:
Norstella is a premier and critical global life sciences data and AI solutions provider dedicated to improving patient access to life-saving therapies. Norstella supports pharmaceutical and biotech companies across the full drug development lifecycle — from pipeline to patient. Our mission is simple: to help our clients bring therapies to market faster and more efficiently, ultimately impacting patient lives.
Norstella unites market-leading brands - Citeline, Evaluate, MMIT, Panalgo, Skipta and The Dedham Group and delivers must-have answers and insights, leveraging AI, for critical strategic, clinical, and commercial decision-making. We help our clients:
- Accelerate the drug development cycle
- Assess competition and bring the right drugs to market
- Make data driven commercial and financial decisions
- Match and recruit patients for clinical trials
- Identify and address barriers to therapies
Norstella serves most pharmaceutical and biotech companies around the world, along with regulators like the FDA, and payers. By providing critical proprietary data supporting AI-driven workflows, Norstella helps clients make decisions faster and with greater confidence. Norstella’s investments in AI are transforming how data is consumed and decisions are made, disrupting inefficient legacy workflows and helping the industry become more efficient, innovative, and responsive to patient needs.
Job Description:
Leads multi-product security efforts, develops the ProdSec team and mentors engineers, coordinating incident response, and shaping DevSecOps practices, with primary focus on securing Norstella's agentic SDLC platform (Forge) and its supporting knowledge-base ecosystem (Sage). This engineer owns the security posture of the autonomous AI agent chain end-to-end: the platform itself, the prompts and orchestration layer, the proprietary domain data the agents consume, and — critically — the source code the agents produce. Acts as the senior security partner to the Forge engineering team, embedding security guardrails into every phase of the autonomous SDLC (Intake Architect Threat-Modeling Dev-Planning Dev QA) so that AI-generated code meets the same compliance, audit, and resilience standards as human-written code in HIPAA, GDPR, SOC 2, and FDA 21 CFR Part 11 environments.
Responsibilities:
- Leads threat modeling across complex architectures, including agent-to-agent communication graphs, tool-use surfaces, MCP server boundaries, and prompt/RAG pipelines, and mentors engineers in risk analysis practices.
- Leads secure development training across product groups — including AI-assisted and AI-generated coding workflows — and evolves standards based on industry trends, OWASP LLM Top 10, and emerging agentic-AI threat research.
- Leads vulnerability management for multiple products, setting priorities and ensuring timely resolution, and tunes the Forge Security Agent's SAST/SCA gating, severity thresholds, and triage-LLM classification logic to keep auto-remediation loops both effective and safe.
- Leads supply chain risk assessments and drives API governance practices across product portfolios, with explicit ownership of the AI/ML supply chain — foundation model providers (Anthropic, others), Auth0, Snyk, Azure DevOps NPM feeds, vector databases, and the Sage KB seed data — including concentration-risk monitoring and contingency planning.
- Leads security integration across CI/CD and multi-cloud platforms, mentoring engineers on automation, and ensures every agentic run produces a reconstructible audit trail sufficient to demonstrate reasonable judgment to SOC 2, HIPAA, GDPR, and Life Sciences auditors.
- Technical IR lead overseeing cross-product incidents, coordinates disclosure processes, and advises leadership, including incidents involving prompt injection, model jailbreak, agent privilege escalation, sensitive data leakage from KB stores, and compromised AI-generated code reaching production.
- Leads AI/ML model security practices across multiple teams and establishes safe design principles, including least-privilege tool-use, secrets handling for agent runtimes, PII/PHI sanitization in prompts and outputs, model-output validation, and red-team / adversarial-input programs targeting the Forge agent chain.
- Leads SSDLC governance initiatives, mentors teams on embedding controls, and validates adherence across product lines, with explicit accountability for the agentic SSDLC: ensuring the Threat Model Production Agent's output is consumed downstream, that the Forge Security Agent's fail-open posture is risk-tiered appropriately, and that human-in-the-loop checkpoints are placed where judgment is genuinely required.
- Designs and operates continuous security evaluation pipelines that benchmark agent runs across safety, compliance, and code-quality dimensions, surfacing regressions in real time.
- Defines acceptance criteria and security guardrails for AI-generated code and agent outputs, ensuring policy compliance and alignment with business intent before production promotion.
- Partners with Agentic Engineering to instrument observability, logging, and tracing across the full agent execution graph so every decision an agent makes is auditable, explainable, and defensible to regulators.
Qualifications:
- Holds a BA/BS degree or equivalent experience; certifications such as CISSP, OSWE, or cloud security specialties preferred. AI/ML security credentials (e.g., AI Security Institute, MLSecOps, or equivalent) a strong plus.
- Typically 4+ years of experience or equivalent leadership across multi-product security programs, with at least 1–2 of those years applied to AI/ML or LLM-powered systems in production.
- Strong knowledge of managing SDLC risks across multiple teams and mentoring others in secure design, including agentic and AI-assisted SDLC patterns where code authorship is partially delegated to LLM agents.
- Proficient in enterprise vulnerability management, guiding priorities and automation strategies, including LLM-mediated triage and auto-remediation loops.
- Strong knowledge of integrating security into complex CI/CD and cloud environments, including containerized agent runtimes, GPU-accelerated inference endpoints, and serverless agent orchestration on AWS, GCP, or Azure.
- Strong knowledge of secure development practices and setting engineering-wide standards.
- Proficient in designing and reviewing encryption and data protection strategies.
- Proficient in automation in product pipelines and mentoring teams on efficiency.
- Proficient in AI/ML security governance across multiple teams, including OWASP LLM Top 10, prompt injection / jailbreak defense, RAG poisoning, model-output sanitization, and adversarial testing of agent chains.
- Proficient in SSDLC governance, leading implementation and mentoring peers on embedding security into development processes, with demonstrated ability to apply SSDLC controls to non-deterministic, agent-driven development workflows.
- Hands-on experience with LLM-powered applications (Claude, GPT-4, Gemini, Llama, or equivalent), including prompt engineering, function/tool calling, and chain-of-thought orchestration — sufficient to security-review them, not just consume them.
- Working familiarity with at least one agentic framework or orchestration toolkit (LangGraph, CrewAI, Autogen, Semantic Kernel, MCP, or comparable), and the threat surface each one introduces.
- Working knowledge of vector databases (Pinecone, Weaviate, Qdrant, pgvector) and retrieval-augmented generation patterns, with an eye to KB poisoning, retrieval leakage, and access-control failures.
- Understanding of LLM evaluation methodologies — behavioral testing, hallucination detection, output grading, drift detection — applied to security and compliance properties of agent outputs.
- Strong software engineering fundamentals in Python and/or TypeScript; able to read and contribute to production agent code, not just review it from a distance.
- Compliance fluency in SOC 2, HIPAA, GDPR, and ideally FDA 21 CFR Part 11 — and the judgment to translate those frameworks into concrete controls for AI/ML and agentic systems.
- Pharma, life sciences, or healthcare data experience (RWD/RWE, clinical trials, market access) is a strong plus given the Norstella product portfolio.
Our Guiding Principles for success at Norstella:
01: Bold, Passionate, and Mission-First
02: Integrity, Truth, and Reality
03: Kindness, Empathy, and Grace
04: Resilience, Mettle, and Perseverance
05: Humility, Gratitude, and Learning
Benefits:
- Medical and Prescription Drug Benefits
- Health Savings Accounts (HSA) or Flexible Spending Accounts (FSA)
- Dental & Vision Benefits
- Basic Life and AD&D Benefits
- 401k Retirement Plan with Company Match
- Company Paid Short & Long-Term Disability
- Paid Parental Leave
- Paid Time Off & Company Holidays
Please Note - All candidates must be authorized to work in the United States. We do not provide visa sponsorship or transfers. We are not currently accepting candidates who are on an OPT visa.
Norstella is an equal opportunity employer. All job applicants will receive equal treatment regardless of race, creed, color, religion, alienage or national origin, ancestry, citizenship status, age, physical or mental disability or handicap, medical condition, sex (including pregnancy and pregnancy-related conditions), marital or domestic partner status, military or veteran status, gender, gender identity or expression, sexual orientation, genetic information, reproductive health decision making, or any other protected characteristic as established by federal, state, or local law.
Sometimes the best opportunities are hidden by self-doubt. We disqualify ourselves before we have the opportunity to be considered. Regardless of where you came from, how you identify, or the path that led you here- you are welcome. If you read this job description and feel passion and excitement, we’re just as excited about you.
All legitimate roles with Norstella will be posted on Norstella’s job board which is located at norstella.com/careers. If a role is not posted on this job board, a candidate should assume the role is not a legitimate role with Norstella. Norstella is not responsible for an application that may be submitted by or through a third-party and candidates should proceed with extreme caution if a third-party approaches them about an open role with Norstella. Norstella will never ask for anything of value or any type of payment during or as part of any recruitment, interview, or pre-hire onboarding process. If you are aware of or have reason to believe a job posting purportedly for a role with Norstella is fraudulent or otherwise not authorized by Norstella, please contact the Company using the following email address: [email protected].
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Norstella, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Norstella AI Hiring
Norstella has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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